Visualización de datos

Carga de paquetes

library(dplyr)
library(udunits2)
library(sf)
library(DT)
library(plotly)
library(leaflet)
library(rgdal)
library(raster)

Obteniendo datos

primates_cr <-
  st_read(
    "https://raw.githubusercontent.com/gf0604-procesamientodatosgeograficos/2021i-datos/main/gbif/primates-cr-registros.csv",
    options = c(
      "X_POSSIBLE_NAMES=decimalLongitude",
      "Y_POSSIBLE_NAMES=decimalLatitude"
    ))

## options:        X_POSSIBLE_NAMES=decimalLongitude Y_POSSIBLE_NAMES=decimalLatitude 
## Reading layer `primates-cr-registros' from data source 
##   `https://raw.githubusercontent.com/gf0604-procesamientodatosgeograficos/2021i-datos/main/gbif/primates-cr-registros.csv' 
##   using driver `CSV'
## Simple feature collection with 4509 features and 50 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: -85.96248 ymin: 8.040197 xmax: -82.55949 ymax: 11.15408
## CRS:           NA

cantones <-
  st_read(
    "https://raw.githubusercontent.com/gf0604-procesamientodatosgeograficos/2021i-datos/main/ign/delimitacion-territorial-administrativa/cr_cantones_simp_wgs84.geojson",
    quiet = TRUE
  )

provincias <-
  st_read(
    "https://raw.githubusercontent.com/gf0604-procesamientodatosgeograficos/2021i-datos/main/ign/delimitacion-territorial-administrativa/cr_provincias_simp_wgs84.geojson",
  )

## Reading layer `cr_provincias_simp_wgs84' from data source 
##   `https://raw.githubusercontent.com/gf0604-procesamientodatosgeograficos/2021i-datos/main/ign/delimitacion-territorial-administrativa/cr_provincias_simp_wgs84.geojson' 
##   using driver `GeoJSON'
## Simple feature collection with 7 features and 7 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -87.10189 ymin: 5.498569 xmax: -82.55287 ymax: 11.21964
## Geodetic CRS:  WGS 84

st_crs(primates_cr) <- 4326
st_crs(cantones) <- 4326
st_crs(provincias) <- 4326

#Cruce datos
primates_cr <- primates_cr %>%
  st_join(cantones["canton"])

Tabla con paquete DT

primates_cr %>%
  st_drop_geometry() %>%
  dplyr::select(family, species, stateProvince, canton, eventDate) %>%
  datatable(colnames = c("Familia", "Especie", "Provincia", "Cantón", "Fecha"),
            options = list(searchHighlight = TRUE,
  language = list(url = '//cdn.datatables.net/plug-ins/1.10.11/i18n/Spanish.json'),
  pageLength = 10))

Gráfico de pastel con Plotly

plot_ly(data = primates_cr, labels = ~species, values = ~4509, 
               type = "pie") %>% 
  layout(title= 'Especies') %>%
  config(locale = "es")

Mapa de distribución

#Datos altitud raster
alt <- getData(
  "worldclim",
  var = "alt",
  res = .5,
  lon = -84,
  lat = 10
)

altitud <- crop(alt, extent(-86, -82.3, 8, 11.3))

#Mapa
primates_cr %>%
  dplyr::select(stateProvince,
         canton,
         eventDate) %>%
  leaflet() %>%
  addProviderTiles(providers$OpenStreetMap.Mapnik, group = "OpenStreetMap") %>%
  addProviderTiles(providers$Stamen.TonerLite, group = "Stamen Toner Lite") %>%
  addProviderTiles(providers$Esri.WorldImagery, group = "Imágenes de ESRI") %>%
  addRasterImage(altitud, col= topo.colors(2), opacity = 0.8, 
                 group = "Altitud") %>%
  addCircleMarkers(
    stroke = F,
    radius = 1,
    fillColor = 'red',
    fillOpacity = 1,
    popup = paste(
      primates_cr$stateProvince,
      primates_cr$canton,
      primates_cr$eventDate,
      sep = '<br/>'
    ),
    group = "Primates"
  ) %>%
  addLayersControl(
    baseGroups = c("OpenStreetMap", "Stamen Toner Lite", "Imágenes de ESRI",
                   "Altitud"),
    overlayGroups = c("Primates")
  ) %>%
  addMiniMap(
    tiles = providers$Stamen.OpenStreetMap.Mapnik,
    position = "bottomleft",
    toggleDisplay = TRUE
  )